In this work we develop an alternative method based on the notion of a continuously parameterized motion primitive [1]}, [2]}, [3]}, [4]} and show the method is compatible with Deep RL. We emphasize the novel use of a variable duration action, where each action-duration is uniquely chosen at every decision point along the horizon. Our proposed method improves planning speed and stability by moving the computational burden off-line. Difficult scenarios are worked out off-line through randomized exploration of the state-space, and the best trajectory formulations are memorized by the policy network.
